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Use Dynamic Bayesian network to estimate the reliability of Adamia Water Network
, 2016 لميعة باقر جواد +1 more
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Performance evaluation of typical automatic technical safety barriers in chemical atmospheric storage tank areas. [PDF]
PLoS OneWang T, Zhang M, Tan X.
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Are we ready for causal discovery in biological systems using deep learning? [PDF]
Brief BioinformYeo HC, Selvarajoo K.
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Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks
Alec Graham Sheffield +4 more
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Advanced Clustering for Mobile Network Optimization: A Systematic Literature Review. [PDF]
Sensors (Basel)Nawej CM, Owolawi PA, Walingo TM.
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Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability. [PDF]
PLoS OneFulton L, Lyu P.
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2021
Underground transportation systems are in great demand in many large cities all over the world. Tunnel construction has presented a powerful momentum for rapid economic development worldwide. However, owing to various risk factors in complex project environments, safety violations occur frequently in tunnel construction, leading to large problems on ...
Limao Zhang +3 more
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Underground transportation systems are in great demand in many large cities all over the world. Tunnel construction has presented a powerful momentum for rapid economic development worldwide. However, owing to various risk factors in complex project environments, safety violations occur frequently in tunnel construction, leading to large problems on ...
Limao Zhang +3 more
openaire +1 more source
Dynamic Bayesian Neural Networks
2020We define an evolving in time Bayesian neural network called a Hidden Markov neural network. The weights of a feed-forward neural network are modelled with the hidden states of a Hidden Markov model, whose observed process is given by the available data.
Rimella, Lorenzo, Whiteley, Nick
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